0 Neolane Offers a New Marketing Automation Option

Neolane, a Paris-based marketing automation software vendor, formally announced its entry to the U.S. market last week. I’ve been tracking Neolane for some time but chose not to write about it until they established a U.S. presence. So now the story can be told.

Neolane is important because it’s a full-scale competitor to Unica and the marketing automation suites of SAS and Teradata, which have pretty much had the high-end market to themselves in recent years. (You might add SmartFocus and Alterian to the list, but they sell mostly to service providers rather than end-users.) The company originally specialized in email marketing but has since broadened to incorporate other channels. Its email heritage still shows in strong content management and personalization capabilities. These are supplemented by powerful collaborative workflow, project management and marketing planning. Like many European products, Neolane was designed from the ground up to support trans-national deployments with specialized features such as multiple languages and currencies. The company, founded in 2001, now has over 100 installed clients. These include many very large firms such as Carrefour, DHL International and Virgin Megastores.

In evaluating enterprise marketing systems, I look at the five sets of capabilities: planning/budgeting; project management; content management; execution, and analysis. (Neolane itself offers a different set of five capabilities, although they are pretty similar.) Let’s go through these in turn.

Neolane does quite well in the first three areas, which all draw on its robust workflow management engine. This engine is part of Neolane’s core technology, which allows tight connections with the rest of the system. By contrast, many Neolane competitors have added these three functions at least in part through acquisition. This often results in less-than-perfect integration among the suite components.

Execution is a huge area, so we’ll break it into pieces. Neolane’s roots in email result in a strong email capability, of course. The company also claims particular strength in mobile (phone) marketing, although it’s not clear this involves more than supporting SMS and MMS output formats. Segmentation and selection features are adequate but not overly impressive: when it comes to really complex queries, Neolane users may find themselves relying on hand-coded SQL statements or graphical flow charts that can quickly become unmanageable. Although most of today’s major campaign management systems have deployed special grid-based interfaces to handle selections with hundreds or thousands of cells, I didn’t see that in Neolane.

On the other hand, Neolane might argue that its content personalization features reduce the need for building so many segments in the first place. Personalization works the same across all media: users embed selection rules within templates for email, Web pages and other messages. This is a fairly standard approach, but Neolane offers a particularly broad set of formats. It also provides different ways to build the rules, ranging from a simple scripting language to a point-and-click query builder. Neolane’s flow charts allow an additional level of personalized treatment, supporting sophisticated multi-step programs complete with branching logic. That part of the system seems quite impressive.

Apart from personalization, Neolane doesn’t seem to offer execution features for channels such as Web sites, call centers and sales automation. Nor, so far as I can tell, does it offer real-time interaction management—that is, gathering information about customer behavior during an interaction and determining an appropriate response. This is still a fairly specialized area and one where the major marketing automation vendors are just now delivering real products, after talking about it for years. This still puts them ahead of Neolane.

Execution also includes creation and management of the marketing database itself. Like most of its competitors, Neolane generally connects to a customer database built by an external system. (The exceptions would be enterprise suites like Oracle/Siebel and SAP, which manage the databases themselves. Yet even they tend to extract operational data into separate structures for marketing purposes.) Neolane does provide administrative tools for users to define database columns and tables, so it’s fairly easy to add new data if there’s no place else to store it. This would usually apply to administrative components such as budgets and planning data or to marketing-generated information such as campaign codes.

Analytics is the final function. Neolane does provide standard reporting. But it relies on connections to third-party software including SPSS and KXEN for more advanced analysis and predictive modeling. This is a fairly common approach and nothing to particularly complain about, although you do need to look closely to ensure that the integration is suitably seamless.

Over all, Neolane does provide a broad set of marketing functions, although it may not be quite as strong as its major competitors in some areas of execution and analytics. Still, it’s a viable new choice in a market that has offered few alternatives in recent years. So for companies considering a new system, it’s definitely worth a look.

0 Proprietary Databases Rise Again

I’ve been noticing for some time that “proprietary” databases are making a come-back in the world of marketing systems. “Proprietary” is a loaded term that generally refers to anything other than the major relational databases: Oracle, SQL Server and DB2, plus some of the open source products like MySQL. In the marketing database world, proprietary systems have a long history tracing back to the mid-1980’s MCIF products from Customer Insight, OKRA Marketing, Harte-Hanks and others. These originally used specialized structures to get adequate performance from the limited PC hardware available in the mid-1980’s. Their spiritual descendants today are Alterian and SmartFocus, both with roots in the mid-1990’s Brann Viper system and both having reinvented themselves in the past few years as low cost / high performance options for service bureaus to offer their clients.

Nearly all the proprietary marketing databases used some version of an inverted (now more commonly called “columnar”) database structure. In such a structure, data for each field (e.g., Customer Name) is physically stored in adjacent blocks on the hard drive, so it can be accessed with a single read. This makes sense for marketing systems, and analytical queries in general, which typically scan all contents of a few fields. By contrast, most transaction processes use a key to find a particular record (row) and read all its elements. Standard relational databases are optimized for such transaction processing and thus store entire rows together on the hard drive, making it easy to retrieve their contents.

Columnar databases themselves date back at least to mid-1970’s products including Computer Corporation of America Model 204, Software AG ADABAS, and Applied Data Research (now CA) Datacom/DB. All of these are still available, incidentally. In an era when hardware was vastly more expensive, the great efficiency of these systems at analytical queries made them highly attractive. But as hardware costs fell and relational databases became increasingly dominant, they fell by the wayside except for a special situations. Their sweet spot of high-volume analytical applications was further invaded by massively parallel systems (Teradata and more recently Netezza) and multi-dimensional data cubes (Cognos Powerplay, Oracle/Hyperion EssBase, etc.). These had different strengths and weaknesses but still competed for some of the same business.

What’s interesting today is that a new generation of proprietary systems is appearing. Vertica has recently gained a great deal of attention due to the involvement of database pioneer Michael Stonebraker, architect of INGRES and POSTGRES. (Click here for an excellent technical analysis by the Winter Corporation; registration required.) QD Technology, launched last year (see my review), isn’t precisely a columnar structure, but uses indexes and compression in a similar fashion. I can’t prove it, but suspect the new interest in alternative approaches is because analytical databases are now getting so large—tens and hundreds of gigabytes—that the efficiency advantages of non-relational systems (which translate into cost savings) are now too great to ignore.

We’ll see where all this leads. One of the few columnar systems introduced in the 1990’s was Expressway (technically, a bit map index—not unlike Model 204), which was purchased by Sybase and is now moderately successful as Sybase IQ. I think Oracle also added some bit-map capabilities during this period, and suspect the other relational database vendors have their versions as well. If columnar approaches continue to gain strength, we can certainly expect the major database vendors to add them as options, even though they are literally orthogonal to standard relational database design. In the meantime, it’s fun to see some new options become available and to hope that costs will come down as new competitors enter the domain of very large analytical databases.

0 Analytica Provides Low-Cost, High-Quality Decision Models

My friends at DM News, which has published my Software Review column for the past fifteen years, unceremoniously informed me this week that they had decided to stop carrying all of their paid columnists, myself included. This caught me in the middle of preparing a review of Lumina Analytica, a pretty interesting piece of simulation modeling software. Lest my research go to waste, I’ll write about Analytica here.

Analytica falls into the general class of software used to build mathematical models of systems or processes, and to then predict the results of a particular set of inputs. Business people typically use such software to understand the expected results of projects such as a new product launch or a marketing campaign, or to forecast the performance of their business as a whole. They can also be used to model the lifecycle of a customer or to calculate the results of key performance indicators as linked on a strategy map, if the relationships among those indicators have been defined with sufficient rigor.

When the relationships between inputs and outputs are simple, such models can be built in a spreadsheet. But even moderately complex business problems are beyond what a spreadsheet can reasonably handle: they have too many inputs and outputs and the relationships among these are too complicated. Analytica makes it relatively easy to specify these relationships by drawing them on an “influence diagram” that looks like a typical flow chart. Objects within the chart, representing the different inputs and outputs, can then be opened up to specify the precise mathematical relationships among the elements.

Analytica can also build models that run over a number of time periods, using results from previous periods as inputs to later periods. You can do something like this in a spreadsheet, but it takes a great many hard-coded formulas which are easy to get wrong and hard to change. Analytica also offers a wealth of tools for dealing with uncertainties, such as many different types of probability distributions. These are virtually impossible to handle in a normal spreadsheet model.

Apart from spreadsheets, Analytica fits between several niches in the software world. Its influence diagrams resemble the pictures drawn by graphics software: but unlike simple drawing programs, Analytica has actual calculations beneath its flow charts. On the other hand, Analytica is less powerful than process modeling software used to simulate manufacturing systems, call center operations, or other business processes. That software has many very sophisticated features tailored to modeling such flows in detail: for example, ways to simulate random variations in the arrival rates of telephone calls or to incorporate the need for one process step to wait until several others have been completed. It may be possible to do much of this in Analytica, but it would probably be stretching the software beyond its natural limits.

What Analytica does well is model specific decisions or business results over time. The diagram-building approach to creating models is quite powerful and intuitive, particularly because users can build modules within their models, so a single object on a high-level diagram actually refers to a separate, detailed diagram of its own. Object attributes include inputs, outputs and formulas describing how the outputs are calculated. Objects can also contain arrays to handle different conditions: for example, a customer object might use arrays to define treatments for different customer segments. This is a very powerful feature, since its lets an apparently simple model capture a great deal of actual detail.

Setting up a model in Analytica isn’t exactly simple, although it may be about as easy as possible given the inherent complexity of the task. Basically, users place the objects on a palette, connect them with arrows, and then open them up to define the details. There are many options within these details, so it does take some effort to learn how to get what you want. The vendor provides a tutorial and detailed manual to help with the learning process, and offers a variety of training and consulting options. Although it is accessible to just about anyway, the system is definitely oriented towards sophisticated users, providing advanced statistical features and methods that no one else would understand.

The other intriguing feature of Analytica is its price. The basic product costs a delightfully reasonable $1,295. Other versions range up to $4,000 including ability to access ODBC data sources, handle very large arrays, and run automated optimization procedures. A server-based version costs $8,000, but only very large companies would need that one.

This pricing is quite impressive. Modeling systems can easily cost tens or hundreds of thousands of dollars, and it’s not clear they provide much more capability than Analytica. On the other hand, Analytica’s output presentation is rather limited—some basic tables and graphs, plus several statistical measures of uncertainty. There’s that statistical orientation again: as a non-statistician, I would have preferred better visualization of results.

In my own work, Analytica could definitely provide a tool for building models to simulate customers’ behaviors as they flow through an Experience Matrix. This is already more than a spreadsheet can handle, and although it could be done in QlikTech it would be a challenge. Similarly, Analytica could be used in business planning and simulation. It wouldn’t be as powerful as a true agent-based model, but could provide an alternative that costs less and is much easier to learn how to build. If you’re in the market for this sort of modeling—particularly if you want to model uncertainties and not just fixed inputs—Analytica is definitely worth a look.

0 Marketing Performance Measurement: No Answers to the Really Tough Questions

I recently ran a pair of two-day workshops on marketing performance measurement. My students had a variety of goals, but the two major ones they mentioned were the toughest issues in marketing: how to allocate resources across different channels and how to measure the impact of marketing on brand value.

Both questions have standard answers. Channel allocation is handled by marketing mix models, which analyze historical data to determine the relative impact of different types of spending. Brand value is measured by assessing the important customer attitudes in a given market and how a particular brand matches those attitudes.

Yet, despite my typically eloquent and detailed explanations, my students found these answers unsatisfactory. Cost was one obstacle for most of them; lack of data was another. They really wanted something simpler.

I’d love to report I gave it to them, but I couldn't. I had researched these topics thoroughly as preparation for the workshops and hadn’t found any alternatives to the standard approaches; further research since then still hasn’t turned up anything else of substance. Channel allocation and brand value are inherently complex and there just are no simple ways to measure them.

The best I could suggest was to use proxy data when a thorough analysis is not possible due to cost or data constraints. For channel allocation, the proxy might be incremental return on investment by channel: switching funds from low ROI to high ROI channels doesn’t really measure the impact of the change in marketing mix, but it should lead to an improvement in the average level of performance. Similarly, surveys to measure changes in customer attitudes toward a brand don’t yield a financial measure of brand value, but do show whether it is improving or getting worse. Some compromise is unavoidable here: companies not willing or able to invest in a rigorous solution must accept that their answers will be imprecise.

This round of answers was little better received than the first. Even ROI and customer attitudes are not always available, and they are particularly hard to measure in multi-channel environments where the result of a particular marketing effort cannot easily be isolated. You can try still simpler measures, such as spending or responses for channel performance or market share for brand value. But these are so far removed from the original question that it’s difficult to present them as meaningful answers.

The other approach I suggested was testing. The goal here is to manufacture data where none exists, thereby creating something to measure. This turned out to be a key concept throughout the performance measurement discussions. Testing also shows that marketers are at least doing something rigorous, thereby helping satisfy critics who feel marketing investments are totally arbitrary. Of course, this is a political rather than analytical approach, but politics are important. The final benefit of testing is it gives a platform for continuous improvement: even though you may not know the absolute value of any particular marketing effort, a test tells whether one option or another is relatively superior. Over time, this allows a measurable gain in results compared with the original levels. Eventually it may provide benchmarks to compare different marketing efforts against each other, helping with both channel allocation and brand value as well.

Even testing isn’t always possible, as my students were quick to point out. My answer at that point was simply that you have to seek situations where you can test: for example, Web efforts are often more measurable than conventional channels. Web results may not mirror results in other channels, because Web customers may themselves be very different from the rest of the world. But this again gets back to the issue of doing the best with the resources at hand: some information is better than none, so long as you keep in mind the limits of what you’re working with.

I also suggested that testing is more possible than marketers sometimes think, if they really make testing a priority. This means selecting channels in part on the basis of whether testing is possible; designing programs so testing is built in; and investing more heavily in test activities themselves (such as incentives for survey participants). This approach may ultimately lead to a bias in favor of testable channels—something that seems excessive at first: you wouldn’t want to discard an effective channel simply because you couldn’t test it. But it makes some sense if you realize that testable channels can be improved continuously, while results in untestable channels are likely to stagnate. Given this dynamic, testable channels will sooner or later become more productive than untestable channels. This holds even if the testable channels are less efficient at the start.

I offered all these considerations to my students, and may have seen a few lightbulbs switch on. It was hard to tell: by the time we had gotten this far into the discussion, everyone was fairly tired. But I think it’s ultimately the best advice I could have given them: focus on testing and measuring what you can, and make the best use possible of the resulting knowledge. It may not directly answer your immediate questions, but you will learn how to make the most effective use of your marketing resources, and that’s the goal you are ultimately pursuing.

0 Tableau Software Makes Good Visualization Easy

I took a close look recently at Tableau data visualization software. I liked Tableau a lot, even though it wasn’t quite what I expected. I had thought of it as a way to build aesthetically-correct charts, according to the precepts set down by Edward Tufte and like-minded visualization gurus such as Stephen Few. But even though Tableau follows many of these principles, it is less for building charts than interactive data exploration.

This is admittedly a pretty subtle distinction, since the exploration is achieved through charts. What I mean is that Tableau is designed to make it very easy to see the results of changing one data element at a time, for example to find whether a particular variable helps to predict an outcome. (That’s a little vague: the example that Tableau uses is analyzing the price of a condominium, and adding variables like square footage, number of rooms, number of baths, location, etc. to see if they explain differences in the sales price.) What makes Tableau special is it automatically redraws the graphs as the data changes, often producing a totally different format, The formats are selected according to the fore-mentioned visualization theories, and for the most part are quite effective.

It may be worth diving a bit deeper into those visualization techniques, although I don’t claim to be an expert. You’ve probably heard some of the gurus’ common criticisms: ‘three dimensional’ bars that don’t mean anything; pie charts and gauges that look pretty but show little information given the space they take up; radar charts that are fundamentally incomprehensible. The underlying premise is that humans are extremely good at finding patterns in visual data, so that is what charts should be used for—not to display specific information, which belongs in tables of numbers. Building on this premise, research shows that people find patterns more easily in certain types of displays: shapes, graduations of color, and spatial relationships work well, but not reading numbers, making subtle size comparisons (e.g., slices in a pie chart), or looking up colors in a key. This approach also implies avoiding components that convey no information, such as the shadows on those ‘3-d’ bar charts, since these can only distract from pattern identification.

In general, these principles work well, although I have trouble with some of the rules that result. For example, grids within charts are largely forbidden, on the theory that charts should only show relative information (patterns) and you don’t need a grid to know whether one bar is higher than another. My problem with that one, in fact, it’s often difficult to compare two bars that are not immediately adjacent, and a grid can help. A grid can also provide a useful reference point, such as showing ‘freezing’ on a temperature chart. The gurus might well allow grid lines in some of those circumstances.

On the other hand, the point about color is very well taken. Americans and Europeans often use red for danger and green for good, but there is nothing intuitive about those—they depend on cultural norms. In China, red is a positive color. Worse, the gurus point out, a significant portion of the population is color-blind and can’t distinguish red from green anyway. They suggest that color intensity is a better way to show gradations, since people naturally understand a continuum from light to dark (even though it may not be clear which end of the scale is good or bad). They also suggest muted rather than bright colors, since it’s easier to see subtle patterns when there is less color contrast. In general, they recommend against using color to display meaning (say, to identify regions on a bar chart) because it takes conscious effort to interpret. Where different items must be shown on the same chart, they would argue that differences in shape are more easily understood.

As I say, Tableau is consistent with these principles, although it does let users make other choices if they insist. There is apparently some very neat technology inside Tableau that builds the charts using a specification language rather than conventional configuration parameters. But this is largely hidden from users, since the graphs are usually designed automatically. It may have some effect on how easily the system can switch from one format to another, and on the range of display options.

The technical feature that does impact Tableau users is its approach to data storage. Basically, it doesn’t have one: that is, it relies on external data stores to hold the information it requires, and issues queries against those sources as required. This was a bit of a disappointment to me, since it means Tableau’s performance really relies on the external systems. Not that that’s so terrible—you could argue (as Tableau does) that this avoids loading data into a proprietary format, making it easier to access the information you need without pre-planning. But it also means that Tableau can be painfully slow when you’re working with large data sets, particularly if they haven’t been optimized for the queries you’re making. In a system designed to encourage unplanned “speed of thought” data exploration, I consider this a significant drawback.

That said, let me repeat that I really liked Tableau. Query speed will be an issue in only some situations. Most of the time, Tableau will draw the required data into memory and work with it there, giving near-immediate response. And if you really need quick response from a very large database, technical staff can always apply the usual optimization techniques. For people with really high-end needs, Tableau already works with the Hyperion multidimensional database and is building an adapter for the Netezza high speed data appliance.

Of course, looking at Tableau led me to compare it with QlikTech. This is definitely apples to oranges: one is a reporting system and the other is a data exploration tool; one has its own database and the other doesn’t. I found that with a little tweaking I could get QlikView to produce many of the same charts as Tableau, although it was certainly more work to get there. I’d love to see the Tableau interface connected with the QlikView data engine, but suspect the peculiarities of both systems make this unlikely. (Tableau queries rely on advanced SQL features; QlikView is not a SQL database.) If I had to choose just one, I would pick the greater data access power and flexibility of QlikTech over the easy visualizations of Tableau. But Tableau is cheap enough—$999 to $1,799 for a single user license, depending on the data sources permitted—that I see no reason most people who need them couldn’t have both.